---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- vision
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: image_segmentation_classifier
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# image_segmentation_classifier

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the taresco/newspaper_ocr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0033
- Accuracy: 0.9993

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0014        | 1.0   | 2031  | 0.0065          | 0.9986   |
| 0.0005        | 2.0   | 4062  | 0.0033          | 0.9993   |
| 0.0003        | 3.0   | 6093  | 0.0058          | 0.9990   |
| 0.0002        | 4.0   | 8124  | 0.0043          | 0.9983   |
| 0.0001        | 5.0   | 10155 | 0.0036          | 0.9990   |


### Framework versions

- Transformers 4.52.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.0